This work presents a photovoltaic greenhouse’s design and performance evaluation as an energy hub in modern agriculture that integrates battery energy storage, an electric vehicle charging station, and non-controlled loads. The greenhouse roof comprises 48 semi-transparent photovoltaic panels with nominal transparency of 20% and 110 W capacity. The control of the photovoltaic greenhouse as an energy hub was approached as an optimization problem with the aim of minimizing the energy purchased from the grid. The simulation results indicate that the system is capable of balancing power transactions within the microgrid, thus enabling electromobility and, at the same time, achieving an average energy saving of up to 41%. Furthermore, it was found that the case of slow charging of the electric vehicle at night was less demanding on the battery system than fast charging during the day in terms of abrupt power transitions and average state of charge of the battery system, 61% vs. 53%, respectively. Empirical results also demonstrated the negative impact of soiling generated by agricultural activity on the performance of solar panels. For a period analyzed of three years, an average annual production loss of 6.8% was calculated.

The stability of stochastic Model Predictive Control (MPC) subject to additive disturbances is often demonstrated in the literature by constructing Lyapunov-like inequalities that ensure closed-loop performance bounds and boundedness of the state, but tight ultimate bounds for the state and non-conservative performance bounds are typically not determined. In this work we use an input-to-state stability property to find conditions that imply convergence with probability 1 of a disturbed nonlinear system to a minimal robust positively invariant set. We discuss implications for the convergence of the state and control laws of stochastic MPC formulations, and we prove convergence results for several existing stochastic MPC formulations for linear and nonlinear systems.

The modular multilevel converter (MMC) is a prominent solution for medium- to high-voltage and high-power conversion applications. Recently, distributed control strategies have been proposed to make this converter modular in terms of software and control hardware. In this control architecture, high-level control tasks are performed by a central controller (CC), whereas low-level control tasks are achieved by local controllers (LCs) placed on the MMC submodules. The CC and LCs use a cyber-physical system (CPS) to share all the necessary information to execute their respective control schemes. In this context, the CPS is vulnerable to cyberattacks, such as the false data injection attack (FDIA), where the data seen by the controllers are corrupted through illegitimate data intrusion. This cyberattack may hinder the MMC performance, producing suboptimal, or even unstable operations. Even more, diverse FDIAs can be generated using artificial intelligence methods to deceive FDIA detectors. This article proposes an FDIA detector based on the reinforcement learning (RL) technique to detect sophisticated FDIAs targeting the MMC control system. The performance of the proposed RL-based FDIA detector is verified via hardware-in-the-loop studies, showing its effectiveness in detecting sophisticated attack sequences affecting the MMC control system.

The performance enhancement of an inverter-based grid-connected system necessitates a fast and accurate dynamic response in terms of estimating three-phase grid voltage attributes. The synchronous reference frame phase-locked loop and/or the frequency-locking (i.e., frequency-locked loop) approaches are widely used in practical applications. However, due to the phase/frequency feedback loops, the aforementioned parameter estimation schemes may experience instability and provide a slow dynamic response. This work presents a PLL-less grid synchronization solution for three-phase applications to counter the slower dynamic response and demonstrate better immunity against the non-ideality of a three-phase grid. In order to remove even and odd-order harmonics and extract the fundamental frequency positive sequence (FFPS), the proposed method employs a combination of band pass filters (CBPF). Additionally, a novel frequency estimation algorithm is developed, which accurately estimates the angular three-phase grid frequency. Furthermore, the phase angle and amplitude are adaptively estimated using an off-line error-resolving approach, which is derived from the transfer function of the proposed pre-filtering solution. Finally, the experimental findings validate the robustness of the current proposal.

The all-pass filter (APF) is an orthogonal signal generator (OSG) structure that is widely adopted in single-phase systems because of its simpler implementation. One of its applications is in the form of synchronous reference frame (SRF) based phase-locked loop (PLL). The APF basically provides unity gain in the whole frequency spectrum while altering its phase according to the frequency. The APF is however devoid of a filtering capability. The DC offset poses a particularly serious challenge and this issue is usually addressed with the integration of additional filetering structures. This paper shows the development of a two-stage modified APF (TSMAPF)-based OSG structure that is capable of suppressing DC offset while maintaining a frequency fixed structure. The proposed OSG relies only on optimized parameter tuning without the need for auxiliary filtering units. The TSMAPF is then combined with a quasi type-1 PLL in order to provide an innovative single-phase PLL with a fast dynamic response and the capacity to reject disturbances. Both simulation and the experimental results verify the effectiveness of the proposed PLL.

This manuscript presents an energy management strategy (EMS) for a commercial smart building participating in a tertiary frequency regulation market. The building integrates non-controllable components, such as loads and photovoltaic generation, and controllable resources such as a battery storage system and a set of electric vehicle (EV) chargers that are available for customers of the smart building. The EMS is based on model predictive control due to its innate ability to deal with operational constraints and different optimization criteria, which are critical for the operation of the EMS, and consists of two stages. The first iteratively optimizes energy costs and revenues from tertiary regulation reserves and activations in order to determine the optimal operation of the smart building and the regulation offers in nominal conditions. Then, a second problem determines the operation whenever an activation request is made. Simulation-based analyses are performed to study the performance of the EMS and its financial viability in diverse scenarios relevant to the smart commercial building. The results show that profits are greater if both upward and downward regulation can be provided, for a larger number of EVs and chargers and for longer connection times. Most notably, incomes from regulation almost match operation costs for a large number of chargers and EVs (240), obtaining a deficit of only EUR 39.12 for a day of operations.